Partially observable reinforcement learning for sustainable active surveillance

Hechang Chen, Bo Yang*, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Active surveillance is the most effective strategy in the applications of infectious disease prevention, road network optimization, crime reconnaissance, etc. However, the incomplete data collected from partially monitored regions by active surveillance disables existing models to maintain a sustainable performance in the future. To address this issue, this article presents a sustainable active surveillance framework (SAS), which consists of a predictor, a classifier, and a planner, by developing a novel partially observable reinforcement learning algorithm. The predictor estimates priorities of candidate regions for monitoring. The classifier assigns candidate regions with similar features into the same groups, so that the data collected from monitored regions can be shared with unmonitored regions within the group. The planner determines where and when to allocate limited resources, considering the outcomes of available resources and model sustainability. An empirical case study on infectious disease prevention showed that the proposed SAS method significantly outperforms the state-of-the-art methods.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 11th International Conference, KSEM 2018, Proceedings
EditorsWeiru Liu, Fausto Giunchiglia, Bo Yang
PublisherSpringer Verlag
Pages425-437
Number of pages13
ISBN (Print)9783319992464
DOIs
Publication statusPublished - 2018
Event11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018 - Changchun, China
Duration: 17 Aug 201819 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11062 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018
Country/TerritoryChina
CityChangchun
Period17/08/1819/08/18

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Neural networks
  • Reinforcement learning
  • Resources allocation
  • Sustainable active surveillance

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